Bootstrap-based improvements for inference with clustered errors

• 成果类型: Article
• 署名作者: Cameron, A. Colin; Gelbach, Jonah B.; Miller, Douglas L.
• 署名单位: University of California System; University of California Davis; University of Arizona
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest.90.3.414
• 发表日期: 2008-08
• 页码: 414-427
• 关键词: in-differences; estimator

摘要

Researchers have increasingly realized the need to account for within-group dependence in estimating standard errors of regression parameter estimates. The usual solution is to calculate cluster-robust standard errors that permit heteroskedasticity and within-cluster error correlation. but presume that the number of clusters is large. Standard asymptotic tests can over-reject, however, with few (five to thirty) clusters. We investigate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the example of Bertrand, Duflo, and Mullai-nathan (2004). Rejection rates of 10% using standard methods can be reduced to the nominal size of 5% using our methods.